They are created by using the yield keyword
next() can be called explicitly or implicitly inside for loop
They can be finite or infinite
🤔 Python generators
What are they? How to use them?
#Python
🧵Let's find out 👇
They are created by using the yield keyword
next() can be called explicitly or implicitly inside for loop
They can be finite or infinite
The state of function is remembered.
For example, the number is incremented and sent back from yield at the consecutive call next()
For example, we don't need to store 1mio elements in memory to do something with each element
It executes only when next() is called
For example, that's why Exception is raised only on the next() call
- __iter__ -> to enable iteration
- __next__ -> to enable next element access
More from Machine learning
10 machine learning YouTube videos.
On libraries, algorithms, and tools.
(If you want to start with machine learning, having a comprehensive set of hands-on tutorials you can always refer to is fundamental.)
🧵👇
1⃣ Notebooks are a fantastic way to code, experiment, and communicate your results.
Take a look at @CoreyMSchafer's fantastic 30-minute tutorial on Jupyter Notebooks.
https://t.co/HqE9yt8TkB
2⃣ The Pandas library is the gold-standard to manipulate structured data.
Check out @joejamesusa's "Pandas Tutorial. Intro to DataFrames."
https://t.co/aOLh0dcGF5
3⃣ Data visualization is key for anyone practicing machine learning.
Check out @blondiebytes's "Learn Matplotlib in 6 minutes" tutorial.
https://t.co/QxjsODI1HB
4⃣ Another trendy data visualization library is Seaborn.
@NewThinkTank put together "Seaborn Tutorial 2020," which I highly recommend.
https://t.co/eAU5NBucbm
On libraries, algorithms, and tools.
(If you want to start with machine learning, having a comprehensive set of hands-on tutorials you can always refer to is fundamental.)
🧵👇
1⃣ Notebooks are a fantastic way to code, experiment, and communicate your results.
Take a look at @CoreyMSchafer's fantastic 30-minute tutorial on Jupyter Notebooks.
https://t.co/HqE9yt8TkB
2⃣ The Pandas library is the gold-standard to manipulate structured data.
Check out @joejamesusa's "Pandas Tutorial. Intro to DataFrames."
https://t.co/aOLh0dcGF5
3⃣ Data visualization is key for anyone practicing machine learning.
Check out @blondiebytes's "Learn Matplotlib in 6 minutes" tutorial.
https://t.co/QxjsODI1HB
4⃣ Another trendy data visualization library is Seaborn.
@NewThinkTank put together "Seaborn Tutorial 2020," which I highly recommend.
https://t.co/eAU5NBucbm
This is a Twitter series on #FoundationsOfML.
❓ Today, I want to start discussing the different types of Machine Learning flavors we can find.
This is a very high-level overview. In later threads, we'll dive deeper into each paradigm... 👇🧵
Last time we talked about how Machine Learning works.
Basically, it's about having some source of experience E for solving a given task T, that allows us to find a program P which is (hopefully) optimal w.r.t. some metric
According to the nature of that experience, we can define different formulations, or flavors, of the learning process.
A useful distinction is whether we have an explicit goal or desired output, which gives rise to the definitions of 1️⃣ Supervised and 2️⃣ Unsupervised Learning 👇
1️⃣ Supervised Learning
In this formulation, the experience E is a collection of input/output pairs, and the task T is defined as a function that produces the right output for any given input.
👉 The underlying assumption is that there is some correlation (or, in general, a computable relation) between the structure of an input and its corresponding output and that it is possible to infer that function or mapping from a sufficiently large number of examples.
❓ Today, I want to start discussing the different types of Machine Learning flavors we can find.
This is a very high-level overview. In later threads, we'll dive deeper into each paradigm... 👇🧵
Last time we talked about how Machine Learning works.
Basically, it's about having some source of experience E for solving a given task T, that allows us to find a program P which is (hopefully) optimal w.r.t. some metric
I'm starting a Twitter series on #FoundationsOfML. Today, I want to answer this simple question.
— Alejandro Piad Morffis (@AlejandroPiad) January 12, 2021
\u2753 What is Machine Learning?
This is my preferred way of explaining it... \U0001f447\U0001f9f5
According to the nature of that experience, we can define different formulations, or flavors, of the learning process.
A useful distinction is whether we have an explicit goal or desired output, which gives rise to the definitions of 1️⃣ Supervised and 2️⃣ Unsupervised Learning 👇
1️⃣ Supervised Learning
In this formulation, the experience E is a collection of input/output pairs, and the task T is defined as a function that produces the right output for any given input.
👉 The underlying assumption is that there is some correlation (or, in general, a computable relation) between the structure of an input and its corresponding output and that it is possible to infer that function or mapping from a sufficiently large number of examples.
